Gene flow in Argentinian sunflowers as revealed by genotyping‐by‐sequencing data
Bibliographic record
Abstract
Abstract Gene flow can have several different applied consequences, ranging from extinction to the escape of transgenes to the evolution of weedy or invasive lineages. Here, we describe patterns of hybridization and gene flow involving domesticated and wild sunflowers in Argentina. To address the risks of introgression of variants from the cultivated sunflower into invasive wild Helianthus, we used genotyping‐by‐sequencing (GBS) to genotype 182 samples from 11 sites in Argentina, along with previously published data from samples from the native range (North America), to determine the native source populations of the Argentinian samples and to detect admixture. We unexpectedly discovered two distinctive forms of H. petiolaris in Argentina, one from H. petiolaris subsp. petiolaris as expected, but the other from an unknown source. Extensive admixture was observed among Argentinian sunflowers, largely confirming phenotypic predictions. While many hybrids are F1s, there were signals consistent with introgression from the domesticated sunflower into H. petiolaris. Whether this introgression is incidental or a causal driver of invasiveness is not yet clear, but it seems likely that genes found in the domesticated sunflower genome (whether engineered or not) will quickly find their way into wild Argentinian sunflower populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".